Knill, Kersten, & Mamassian (Chapter 6) provide an interesting discussion of how the Bayesian formulation can be used to help investigate human vision. In their view, computational theories can be based on an ideal observer that uses Bayesian inference to make optimal use of available information. Four factors are important here: the image information used, the output structures estimated, the priors assumed (i.e., knowledge about the structure of the world), and the likelihood function used (i.e., knowledge (...) about the projection of the world onto the sensors). Knill & Kersten argue that such a framework not only helps analyze a perceptual task, but can also help investigators to define it. Two examples are provided (the interpretation of surface contour and the perception of moving shadows) to show how this approach can be used in practice. As the authors admit, most (if not all) perceptual processes are ill-suited to a "strong" Bayesian approach based on a single consistent model of the world. Instead, they argue for a "weak" variant that assumes Bayesian inference to be carried out in modules of more limited scope. But how weak is "weak"? Are such approaches suitable for only a few relatively low-level tasks, or can they be applied more generally? Could a weak Bayesian approach, for example, explain how we would recognize the return of Elvis Presley? The formal modelling of human perception To help get a fix on things, it is useful to examine the fate of an earlier attempt to formalize human perception: the application of information theory. It was once hoped that this theory—a close cousin of the Bayesian formulation—would provide a way to uncover information-handling laws that were largely independent of physical implementation. In this approach, the human nervous system was assumed to have.. (shrink)

When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to discriminate the (...) unattended stimulus, they rely less on prior information about the probed stimulus’ identity. The model is in part inspired by recent findings that attention reduces trial-by-trial variability of the neuronal population response and that they use a common criterion for attended and unattended trials. In five different visual discrimination experiments, when attention was directed away from the target stimulus, subjects did not adjust their response bias in reaction to a change in stimulus presentation frequency despite being fully informed and despite the presence of performance feedback and monetary and social incentives. This indicates that subjects did not rely more on the priors under conditions of inattention as would be predicted by a Bayes-optimal observer model. These results inform and constrain future models of Bayesian inference in the human brain. (shrink)

Why are conditional degrees of belief in an observation E, given a statistical hypothesis H, aligned with the objective probabilities expressed by H? After showing that standard replies are not satisfactory, I develop a suppositional analysis of conditional degree of belief, transferring Ramsey’s classical proposal to statistical inference. The analysis saves the alignment, explains the role of chance-credence coordination, and rebuts the charge of arbitrary assessment of evidence in Bayesian inference. Finally, I explore the implications of this analysis for (...) Bayesian reasoning with idealized models in science. (shrink)

Bayesian epistemologists support the norms of probabilism and conditionalization using Dutch book and accuracy arguments. These arguments assume that rationality requires agents to maximize practical or epistemic value in every doxastic state, which is evaluated from a subjective point of view (e.g., the agent’s expectancy of value). The accuracy arguments also presuppose that agents are opinionated. The goal of this paper is to discuss the assumptions of these arguments, including the measure of epistemic value. I have designed AI agents (...) based on the Bayesian model and a nonmonotonic framework and tested how they achieve practical and epistemic value in conditions in which an alternative set of assumptions holds. In one of the tested conditions, the nonmonotonic agent, which is not opinionated and fulfills neither probabilism nor conditionalization, outperforms the Bayesian in the measure of epistemic value that I argue for in the paper (α-value). I discuss the consequences of these results for the epistemology of rationality. (shrink)

The gambler’s fallacy is the tendency to expect random processes to switch more often than they actually do—for example, to think that after a string of tails, a heads is more likely. It’s often taken to be evidence for irrationality. It isn’t. Rather, it’s to be expected from a group of Bayesians who begin with causal uncertainty, and then observe unbiased data from an (in fact) statistically independent process. Although they converge toward the truth, they do so in an asymmetric (...) way—ruling out “streaky” hypotheses more quickly than “switchy” ones. As a result, the majority (and the average) exhibit the gambler’s fallacy. If they have limited memory, this tendency persists even with arbitrarily-large amounts of data. Indeed, such Bayesians exhibit a variety of the empirical trends found in studies of the gambler’s fallacy: they expect switches after short streaks but continuations after long ones; these nonlinear expectations vary with their familiarity with the causal system; their predictions depend on the sequence they’ve just seen; they produce sequences that are too switchy; and they exhibit greater rates of gambler’s reasoning when forming binary predictions than when forming probability estimates. In short: what’s been thought to be evidence for irrationality may instead be rational responses to limited data and memory. (shrink)

There are two distinct approaches to Bayesian modelling in cognitive science. Black-box approaches use Bayesian theory to model the relationship between the inputs and outputs of a cognitive system without reference to the mediating causal processes; while mechanistic approaches make claims about the neural mechanisms which generate the outputs from the inputs. This paper concerns the relationship between these two approaches. We argue that the dominant trend in the philosophical literature, which characterizes the relationship between black-box and mechanistic (...) approaches to Bayesian cognitive science in terms of the dichotomy between instrumentalism and realism, is misguided. We propose that the two distinctions are orthogonal: black-box and mechanistic approaches to Bayesian modelling can each be given either an instrumentalist or a realist interpretation. We argue that the current tendency to conflate black-box approaches with instrumentalism and mechanistic approaches with realism stems from unwarranted assumptions about the nature of scientific explanation, the ontological commitments of scientific theories, and the role of abstraction and idealization in scientific models. We challenge each of these assumptions to reframe the debates over Bayesian modelling in cognitive science. (shrink)

Under the independence and competence assumptions of Condorcet’s classical jury model, the probability of a correct majority decision converges to certainty as the jury size increases, a seemingly unrealistic result. Using Bayesian networks, we argue that the model’s independence assumption requires that the state of the world (guilty or not guilty) is the latest common cause of all jurors’ votes. But often – arguably in all courtroom cases and in many expert panels – the latest such common cause is (...) a shared ‘body of evidence’ observed by the jurors. In the corresponding Bayesian network, the votes are direct descendants not of the state of the world, but of the body of evidence, which in turn is a direct descendant of the state of the world. We develop a model of jury decisions based on this Bayesian network. Our model permits the possibility of misleading evidence, even for a maximally competent observer, which cannot easily be accommodated in the classical model. We prove that (i) the probability of a correct majority verdict converges to the probability that the body of evidence is not misleading, a value typically below 1; (ii) depending on the required threshold of ‘no reasonable doubt’, it may be impossible, even in an arbitrarily large jury, to establish guilt of a defendant ‘beyond any reasonable doubt’. (shrink)

The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of (...) computational explanation, I argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler. (shrink)

Extant literature suggests a positive correlation between social trust (also called generalized trust) and life satisfaction. However, the psychological pathways underlying this relationship can be complex. Using the Bayesian Mindsponge Framework (BMF), we examined the influence of social trust in a high-violence environment. Employing Bayesian analysis on a sample of 1237 adults in Cali, Colombia, we found that in a linear relationship, generalized trust is positively associated with life satisfaction. However, in a model including the interactions between trust (...) and education level as well as between trust and socioeconomic status, generalized trust is found to be negatively associated with life satisfaction. In this non-linear relationship, both education level and socioeconomic status have moderating effects against the negative association between generalized trust and life satisfaction. In other words, less educated people living in worse socioeconomic conditions are more likely to have lower life satisfaction when they have higher levels of social trust. In contrast, highly educated people living in better socioeconomic conditions are more likely to have higher life satisfaction when they have higher levels of social trust. Due to the facilitating function of trust in information processing, lowering the rigor of the filtering system in a high-violence social environment will likely put an individual at risk. Based on our findings, we suggest that policymakers should consider the impacts of social contexts when advocating for increasing social trust. We also recommend that researchers carefully examine the psychological mechanism underlying an observed association before making suggestions for policymaking. (shrink)

According to standard rational choice theory, as commonly used in political science and economics, an agent's fundamental preferences are exogenously fixed, and any preference change over decision options is due to Bayesian information learning. Although elegant and parsimonious, such a model fails to account for preference change driven by experiences or psychological changes distinct from information learning. We develop a model of non-informational preference change. Alternatives are modelled as points in some multidimensional space, only some of whose dimensions play (...) a role in shaping the agentís preferences. Any change in these "motivationally salient" dimensions can change the agent's preferences. How it does so is described by a new representation theorem. Our model not only captures a wide range of frequently observed phenomena, but also generalizes some standard representations of preferences in political science and economics. (shrink)

In engineering, as in other scientific fields, researchers seek to confirm their models with real-world data. It is common practice to assess models in terms of the distance between the model outputs and the corresponding experimental observations. An important question that arises is whether the model should then be ‘tuned’, in the sense of estimating the values of free parameters to get a better fit with the data, and furthermore whether the tuned model can be confirmed with the (...) same data used to tune it. This dual use of data is often disparagingly referred to as ‘double-counting’. Here, we analyse these issues, with reference to selected research articles in engineering. Our example studies illustrate more and less controversial practices of model tuning and double-counting, both of which, we argue, can be shown to be legitimate within a Bayesian framework. The question nonetheless remains as to whether the implied scientific assumptions in each case are apt from the engineering point of view. (shrink)

The Gompertz distribution is commonly used in biology for modeling fatigue and mortality. This paper studies a class of models proposed by Adham and Walker, featuring a Gompertz type distribution where the dependence structure is modeled by a lognormal distribution, and develops a new multivariate formulation that facilitates several numerical and computational aspects. This paper also implements the FBST, the Full Bayesian Significance Test for pertinent sharp (precise) hypotheses on the lognormal covariance structure. The FBST’s e-value, ev(H), gives (...) the epistemic value of hypothesis, H, or the value of evidence in the observed in support of H. (shrink)

This paper explores the integration of Michael Strevens' concept of idealizations with my previous framework of similarity spaces and context-dependence to develop a comprehensive account of ideal explanations in scientific practice. Idealizations, which involve deliberate falsifications, play a crucial role in distinguishing between causally relevant and irrelevant factors in scientific models. Context-dependent mapping provides a structured approach to handling complementarities and context-dependent phenomena by mapping different observational contexts to distinct sets of physical laws. By combining these two ideas, (...) I will construct an idealized context-dependent mapping structure and discuss how ideal similarity spaces within the framework of context-dependent mapping can enhance our understanding of complex scientific phenomena, especially those involving wave-particle duality and black hole complementarity. I also aim to discuss the types of idealizations that may exist within explanation and examine their relations to the mapping. (shrink)

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose their vulnerabilities and (...) future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their cor- responding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model. (shrink)

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, users anthropomorphise (...) them. For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. (shrink)

A difficult problem for contractualists is how to provide an interpretation of the contractual situation that is both subject to appropriately stringent constraints and yet also appropriately sensitive to certain features of us as we actually are. My suggestion is that we should embrace a model of contractualism that is structurally analogous to the “advice model” of the ideal observer theory famously proposed by Michael Smith (1994; 1995). An advice model of contractualism is appealing since it promises to (...) deliver a straightforward solution to the so-called “conditional fallacy.” But it faces some formidable challenges. On the face of it, it seems to be straightforwardly conceptually incoherent. And it seems to deliver a solution to the conditional fallacy at the cost of being vulnerable to what I shall call “the concessional fallacy.” I shall consider how, if at all, these challenges are to be met. I shall then conclude by considering what this might mean for the so-called “ideal/non-ideal theory” issue. (shrink)

The majority of analyses investigating the professionalisation of scientific domains tend to assume the linear and general features of this transformation. These studies focus on the shift from a non-professionalised state to a professionalised state. This dual approach, however, crucially lacks some other aspects of the process of professionalisation. This issue is discussed within the context of the growth of prehistoric archaeology in France from the 1940s, by observing scientific societies, national research organisations and their social networks. Looking at the (...) scale of Georges Laplace’s career and that of his research group, which studied the typologie analytique method, this article demonstrates the benefits of a ternary model which also encompasses the modes of refusal of professionalisation. (shrink)

The Capgras delusion is a condition in which a person believes that an imposter has replaced some close friend or relative. Recent theorists have appealed to Bayesianism to help explain both why a subject with the Capgras delusion adopts this delusional belief and why it persists despite counter-evidence. The Bayesian approach is useful for addressing these questions; however, the main proposal of this essay is that Capgras subjects also have a delusional conception of epistemic possibility, more specifically, they think (...) more things are possible, given what is known, than non-delusional subjects do. I argue that this is a central way in which their thinking departs from ordinary cognition and that it cannot be characterized in Bayesian terms. Thus, in order to fully understand the cognitive processing involved in the Capgras delusion, we must move beyond Bayesianism. 1 The Simple Bayesian Model2 Anomalous Evidence and the Capgras Delusion3 Impaired Reasoning4 Setting Priors5 Epistemic Modality6 Delusions of Possibility7 Delusions of Possibility in Different Contexts8 How Many Factors? (shrink)

According to [Bayesian] models” in cognitive neuroscience, says a recent textbook, “the human mind behaves like a capable data scientist”. Do they? That is to say, do such model show we are rational? I argue that Bayesian models of cognition, perhaps surprisingly, do not and indeed cannot, show that we are Bayesian-rational. The key reason is that such models appeal to approximations, a fact that carries significant implications. After outlining the argument, I critique two (...) responses, seen in recent cognitive neuroscience. One says that the mind can be seen as approximately Bayes-rational, while the other reconceives norms of rationality. (shrink)

Bayesian models can be related to cognitive processes in a variety of ways that can be usefully understood in terms of Marr's distinction among three levels of explanation: computational, algorithmic and implementation. In this note, we discuss how an integrated probabilistic account of the different levels of explanation in cognitive science is resulting, at least for the current research practice, in a sort of unpredicted epistemological shift with respect to Marr's original proposal.

I argue that recent virtue theories (including those of Hursthouse, Slote, and Swanton) face important initial difficulties in accommodating the supererogatory. In particular, I consider several potential characterizations of the supererogatory modeled upon these familiar virtue theories (and their accounts of rightness) and argue that they fail to provide an adequate account of supererogation. In the second half of the paper I sketch an alternative virtue-based characterization of supererogation, one that is grounded in the attitudes of virtuous ideal observers, (...) and that avoids the concerns raised in the first part of the paper. (shrink)

Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt (...) to make them consistent in terms of variables, causal assumptions or parameterization. The approach involves assessing whether competing models of legal arguments are explained or predict facts uncovered before or during the trial process. Those models that are more heavily disconfirmed by the facts are given lower weight, as model plausibility measures, in the Bayesian model comparison and averaging framework adopted. In this way a plurality of arguments is allowed yet a single judgement based on all arguments is possible and rational. (shrink)

Although some previous studies have investigated the relationship between moral foundations and moral judgment development, the methods used have not been able to fully explore the relationship. In the present study, we used Bayesian Model Averaging (BMA) in order to address the limitations in traditional regression methods that have been used previously. Results showed consistency with previous findings that binding foundations are negatively correlated with post-conventional moral reasoning and positively correlated with maintaining norms and personal interest schemas. In addition (...) to previous studies, our results showed a positive correlation for individualizing foundations and post-conventional moral reasoning. Implications are discussed as well as a detailed explanation of the novel BMA method in order to allow others in the field of moral education to be able to use it in their own studies. (shrink)

Evaluative studies of inductive inferences have been pursued extensively with mathematical rigor in many disciplines, such as statistics, econometrics, computer science, and formal epistemology. Attempts have been made in those disciplines to justify many different kinds of inductive inferences, to varying extents. But somehow those disciplines have said almost nothing to justify a most familiar kind of induction, an example of which is this: “We’ve seen this many ravens and they all are black, so all ravens are black.” This is (...) enumerative induction in itsfullstrength. For it does not settle with a weaker conclusion (such as “the ravens observed in the future will all be black”); nor does it proceed with any additional premise (such as the statistical IID assumption). The goal of this paper is to take some initial steps toward a justification for the full version of enumerative induction, against counterinduction, and against the skeptical policy. The idea is to explore various epistemic ideals, mathematically defined as different modes of convergence to the truth, and look for one that is weak enough to be achievable and strong enough to justify a norm that governs both the long run and the short run. So the proposal is learning-theoretic in essence, but a Bayesian version is developed as well. (shrink)

I build on Adam Smith’s account of the impartial spectator in The Theory of Moral Sentiments in order to offer a modest ideal observer theory of moral judgment that is adequate in the following sense: the account specifies the hypothetical conditions that guarantee the authoritativeness of an agent’s (or agents’) responses in constituting the standard in question, and, if an actual agent or an actual community of agents are not under those conditions, their responses are not authoritative in (...) setting this standard. However, in the account that I provide, the hypothetical conditions can themselves be constructed from the psychology and interactions of actual human beings. In other words, facts about the morally appropriate and inappropriate are determined from hypothetical conditions that––while agents in a given society might have yet to attain them––can be constructed from those agents’ shared experiences. Thus, the account offers both an attainable standard of moral judgment and a standard that can transcend the biases of the society which gave rise to it. I also defend the account against three challenges: (a) ideal observer theories do not offer the right kind of motivation to act on the verdicts of the ideal observer; (b) ideal observer theories cannot explain why the idealization in question is well-motivated and not objectionably ad hoc; (c) the standard used in ideal observer theories cannot be defended upon further reflection, because we would need a non-arbitrary, second-order standard to govern our reflection on the first-order standard of moral judgment. (shrink)

Virtue theorists in ethics often embrace the following characterizationof right action: An action is right iff a virtuous agent would performthat action in like circumstances. Zagzebski offers a parallel virtue-basedaccount of epistemically justified belief. Such proposals are severely flawedbecause virtuous agents in adverse circumstances, or through lack ofknowledge can perform poorly. I propose an alternative virtue-based accountaccording to which an action is right (a belief is justified) for an agentin a given situation iff an unimpaired, fully-informed virtuous observerwould deem the (...) action to be right (the belief to be justified). (shrink)

Moral response-dependent metaethical theories characterize moral properties in terms of the reactions of certain classes of individuals. Nick Zangwill has argued that such theories are flawed: they are unable to accommodate the motive of duty. That is, they are unable to provide a suitable reason for anyone to perform morally right actions simply because they are morally right. I argue that Zangwill ignores significant differences between various approvals, and various individuals, and that moral response-dependent theories can accommodate the motive of (...) duty. (shrink)

In recent discussions of whether our lives are or can be meaningful, appeals are often made to such things as “a view from nowhere,” or “the viewpoint of the universe.” In this paper I attempt to make sense of what it might mean for a being to possess such a perspective, and argue that common appeals to such perspectives are inadequately developed; crucially, they do not adequately account for the character of the beings taken to possess these viewpoints. In the (...) second half of the paper I turn to an alternative proposal, one that focuses on the attitudes of virtuous ideal observers in determining the normative statuses of our lives and activities, and argue that it provides a plausible account of meaningfulness. (shrink)

We propose a novel framework that formalizes awareness within Relational Quantum Dynamics (RQD) by integrating Integrated Information Theory (IIT) with quantum measurement processes. In our approach, quantum interactions are not merely stochastic events, but are interpreted as intrinsic awareness updates that occur via Bayesian conditioning. Specifically, we define an awareness metric A(A : B) that combines the quantum mutual information generated during an interaction and Φ as a measure of integrated information. By modeling quantum measurements as stochastic processes and (...) determinizing these via outcome indexing, we construct a composite functor that maps probabilistic quantum instruments to deterministic awareness updates in a separate category of awareness states. We show that this functor preserves identities and composition, ensuring that multi-observer scenarios such as nested Wigner’s Friend or Frauchiger-Renner experiments produce a coherent alignment of facts once interactions occur, even though no single truth value exists prior to interaction. We outline experimental proposals, including nested observer experiments, gravitational entanglement tests, and the engineering of artificial observers with tunable integrated information, to test the predictions of our model. Our framework does not alter standard quantum mechanics but enriches its interpretation by positing that every quantum interaction is accompanied by an awareness update whose significance depends on both the amount of exchanged information and the integration capacity of the systems involved. This work provides a mathematically robust bridge between quantum foundations and cognitive science, suggesting that conscious experience may emerge naturally from the dynamics of quantum information. (shrink)

According to the Theory of the Second Best, in non-ideal circumstances, approximating ideals might be suboptimal (with respect to a specific interpretation of what “approximating an ideal” means). In this paper, I argue that the formal model underlying the Theory can apply to problems in epistemology. Two applications are discussed: First, in some circumstances, second-best problems arise in Bayesian settings. Second, the division of epistemic labour can be subject to second-best problems. These results matter. They allow us (...) to evaluate the claim, made by many philosophers, that second-best problems have import in epistemology (and the specific conditions under which the Theory finds applications). They also allow us to see that talk of “approximating an ideal” is ambiguous, and to clarify the conditions in which approximating an epistemic ideal might be beneficial. (shrink)

: In this paper I attempt to defuse a set of epistemic worries commonly raised against ideal observer theories. The worries arise because of the omniscience often attributed to ideal observers – how can we, as finite humans, ever have access to the moral judgements or reactions of omniscient beings? I argue that many of the same concerns arise with respect to other moral theories (and that these concerns do not in fact reveal genuine flaws in any (...) of these theories), and further, that we can and often do have knowledge of the reactions of ideal observers (according to standard, prominent theories in the domain of epistemology). (shrink)

An ideal constructor produces geometry from scratch, modelled through the bottom-up assembly of a graph-like lattice within a space that is defined, bootstrap-wise, by that lattice. Construction becomes the problem of assembling a homogeneous lattice in three-dimensional space; that becomes the problem of resolving geometrical frustration in quasicrystalline structure; achieved by reconceiving the lattice as a dynamical system. The resulting construction is presented as the introductory model sufficient to motivate the formal argument that it is a fundamental structure; based (...) on which, it is proposed that where mathematics’ numbers conventionally correspond to dimensionless points on the stateless number line, numbers more fundamentally correspond to an ordering of discrete objects constructed within the stateful number lattice. A second observation is that this fundamental lattice structure is helically configured with fractal character, which, as it relates to the geometry underlying spacetime, has relevance to questions in physics, particularly those involving wave-particle duality. (shrink)

How should we update our beliefs when we learn new evidence? Bayesian confirmation theory provides a widely accepted and well understood answer – we should conditionalize. But this theory has a problem with self-locating beliefs, beliefs that tell you where you are in the world, as opposed to what the world is like. To see the problem, consider your current belief that it is January. You might be absolutely, 100%, sure that it is January. But you will soon believe (...) it is February. This type of belief change cannot be modelled by conditionalization. We need some new principles of belief change for this kind of case, which I call belief mutation. In part 1, I defend the Relevance-Limiting Thesis, which says that a change in a purely self-locating belief of the kind that results in belief mutation should not shift your degree of belief in a non-self-locating belief, which can only change by conditionalization. My method is to give detailed analyses of the puzzles which threaten this thesis: Duplication, Sleeping Beauty, and The Prisoner. This also requires giving my own theory of observation selection effects. In part 2, I argue that when self-locating evidence is learnt from a position of uncertainty, it should be conditionalized on in the normal way. I defend this position by applying it to various cases where such evidence is found. I defend the Halfer position in Sleeping Beauty, and I defend the Doomsday Argument and the Fine-Tuning Argument. (shrink)

The "myside bias'' in evaluating arguments is an empirically well-confirmed phenomenon that consists of overweighting arguments that endorse one's beliefs or attack alternative beliefs while underweighting arguments that attack one's beliefs or defend alternative beliefs. This paper makes two contributions: First, it proposes a probabilistic model that adequately captures three salient features of myside bias in argument evaluation. Second, it provides a Bayesian justification of this model, thus showing that myside bias has a rational Bayesian explanation under certain (...) conditions. (shrink)

Predictive processing (PP) has been repeatedly presented as a unificatory account of perception, action, and cognition. In this paper, we argue that this is premature: As a unifying theory, PP fails to deliver general, simple, homogeneous, and systematic explanations. By examining its current trajectory of development, we conclude that PP remains only loosely connected both to its computational framework and to its hypothetical biological underpinnings, which makes its fundamentals unclear. Instead of offering explanations that refer to the same set of (...) principles, we observe systematic equivocations in PP‐based models, or outright contradictions with its avowed principles. To make matters worse, PP‐based models are seldom empirically validated, and they are frequently offered as mere just‐so stories. The large number of PP‐based models is thus not evidence of theoretical progress in unifying perception, action, and cognition. On the contrary, we maintain that the gap between theory and its biological and computational bases contributes to the arrested development of PP as a unificatory theory. Thus, we urge the defenders of PP to focus on its critical problems instead of offering mere re‐descriptions of known phenomena, and to validate their models against possible alternative explanations that stem from different theoretical assumptions. Otherwise, PP will ultimately fail as a unified theory of cognition. (shrink)

Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.

In the social sciences, within the explanatory paradigm of structural individualism, a theory of action – like rational choice theory – models how individuals behave and interact at the micro level in order to explain macro observations as the aggregation of these individuals actions. A central epistemological issue is that such theoretical models are stuck in a dilemma between falsity of their basic assumptions and triviality of their explanation. On the one hand, models which have a great (...) empirical success often rest on unrealistic or even knowingly false assumptions; on the other hand, more complex models, with additional more realistic hypotheses, can (trivially) adapt to a wide range of situations and thus loose their explanatory power. Our purpose here is epistemological and consists in wondering to which extent demanding realistic assumptions in such cases is a relevant criterion with respect to the acceptance of a given explanatory model. Via an analogical reasoning with physics, we argue that this criterion seems too strong and actually irrelevant. General physical principles are not just idealized or unrealistic, they can also be formulated in many different yet equivalent ways which do not imply the same fundamental unobservable entities or phenomena. However, the classification of phenomena that such principles allow to highlight does not depend, at the end, on any particular formulation of these basic assumptions. This suggests that some hypotheses in theoretical models are actually not genuine empirical statements that could be independently tested but only substrates of modeling embodying a classification principle. Thus, we develop a structural invariance criterion that we then apply to rational choice models in the social sciences. We argue that this criterion allows to escape from the epistemological dilemma without condemning formal approaches like rational choice theory for their lack of realisticness nor being stuck to any antirealist viewpoint. (shrink)

To reconcile the standard possible worlds model of knowledge with the intuition that ordinary agents fall far short of logical omniscience, a Stalnakerian strategy appeals to two components. The first is the idea that mathematical and logical knowledge is at bottom metalinguistic knowledge. The second is the idea that non-ideal minds are often fragmented. In this paper, we investigate this Stalnakerian reconciliation strategy and argue, ultimately, that it fails. We are not the first to complain about the Stalnakerian strategy. (...) But in contrast to existing complaints, we want to cause trouble for the strategy directly on its home turf. That is, we will advance our objection while granting both the plausibility of the fragmentation component—save for an extreme version of it—and that of the metalinguistic component. Once our central objection to the Stalnakerian strategy is in place, we will show how it negatively affects Adam Elga and Augustín Rayo’s recent attempt to apply the Stalnakerian strategy in the context of Bayesian decision theory. (shrink)

Background Consent policies for post-mortem organ procurement (OP) vary throughout Europe, and yet no studies have empirically evaluated the ethical implications of contrasting consent models. To fill this gap, we introduce a novel indicator of governance quality based on the ideal of informed support, and examine national differences on this measure through a quantitative survey of OP policy informedness and preferences in seven European countries. -/- Methods Between 2017–2019, we conducted a convenience sample survey of students (n = (...) 2006) in Austria (AT), Belgium (BE), Denmark (DK), Germany (DE), Greece (GR), Slovenia (SI) and Spain (ES), asking participants about their donation preferences, as well as their beliefs and views about the policy in place. From these measures, we computed indices of informedness, policy support, and fulfilment of unexpressed preferences, which we compared across countries and consent systems. -/- Results Our study introduces a tool for analyzing policy governance in the context of OP. Wide variation in policy awareness was observed: Most respondents in DK, DE, AT and BE correctly identified the policy in place, while those in SI, GR and ES did not. Respondents in opt-out countries (AT, BE, ES and GR) tended to support the policy in place (with one exception, i.e., SI), whereas those in opt-in countries (DE and DK) overwhelmingly opposed it. These results reveal stark differences in governance quality across countries and consent policies: We found a preponderance of informed opposition in opt-in countries and a general tendency towards support–either informed or uninformed–in opt-out countries. We also found informed divergence in opt-in countries and a tendency for convergence–either informed or uninformed–among opt-out countries. -/- Conclusion Our study offers a novel tool for analyzing governance quality and illustrates, in the context of OP, how the strengths and weaknesses of different policy implementations can be estimated and compared using quantitative survey data. (shrink)

I discuss Mary Hess’s interaction view of scientific metaphor, outline an alternative view and show how it may prove fruitful when applied to chapters of the history of science. I start with a reconstruction of the discussion on the nature of scientific models and their relationship to metaphors that took place in the Anglo-Saxon philosophy of Science starting from the Fifties; the discovery began with Stephen Pepper and Kenneth Burke, reaching Thomas Kuhn, Marx Wartofsky, and George Lakoff via Max (...) Black's and Mary Hesse's interaction view. I argue that Hesse's view has a number of weak points: uncritically accepting Black's idea of "interaction", for keeping too much of a logical empiricist view of observation language, and for keeping too much of a Scholastic distinction between formal and material analogy. I argue that a few insights from Kuhn and Wartofsky seem to go in the same direction as Granger's view of metaphor as opposed to metonymy, thus severing the link between metaphor and the "qualitative". I emphasise that the Cartesian legacy and its implicit ideal of a "mirroring" relationship between the world and the mind is the main obstacle to be eventually overcome if we are to work out a satisfactory account of the language of science and that suggestions from pragmatism as well as from French epistemology may be used in order to work out a post-Cartesian view of science. -/- . (shrink)

When an agent learns of an expert's credence in a proposition about which they are an expert, the agent should defer to the expert and adopt that credence as their own. This is a popular thought about how agents ought to respond to (ideal) experts. In a Bayesian framework, it is often modelled by endowing the agent with a set of priors that achieves this result. But this model faces a number of challenges, especially when applied to non- (...) class='Hi'>ideal agents (who nevertheless interact with ideal experts). I outline these problems, and use them as desiderata for the development of a new model. Taking inspiration from Richard Jeffrey's development of Jeffrey conditioning, I develop a model in which expert reports are taken as exogenous constraints on the agent's posterior probabilities. I show how this model can handle a much wider class of expert reports (for example reports of conditional probabilities), and can be naturally extended to cover propositions for which the agent has no prior. (shrink)

I start with a reconstruction of the discussion on the nature of scientific models and on their relationship to metaphors that has taken place in the Anglo-Saxon philosophy of Science starting from the Fifties; the discovery started with Stephen Pepper and Kenneth Burke, reaching Thomas Kuhn, Marx Wartofsky, and George Lakoff via Max Black's and Mary Hesse's interaction view. I argue that Hesse's view has a number of weak points: uncritically accepting Black's idea of "interaction", for keeping too much (...) of a logical empiricist view of observation language, and for keeping too much of a Scholastic distinction between formal and material analogy. I argue that a few insights from Kuhn and Wartofsky seem to go in the same direction as Granger's view of metaphor as opposed to metonymy, thus severing the link between metaphor and the "qualitative". I argue that the Cartesian legacy and its implicit ideal of a "mirroring" relationship between the world and the mind is the main obstacle to be eventually overcome if we are to work out a satisfactory account of the language of science and that suggestions from pragmatism as well as from French epistemology may be used in order to work out a post-Cartesian view of science. -/- . (shrink)

In 2012, a major traffic safety organization tasked the MIT AgeLab with developing a data-driven system for rating the effectiveness of new technologies intended to improve safety. Such a system was envisioned as having the potential to educate and guide consumers towards more confident and strategic purchasing decisions, ideally encouraging adoption of technologies with demonstrated safety benefit. In addition, an evaluation of the status and extent of existing data was seen as a way of identifying research gaps in the state (...) of knowledge about safety systems. The focus was on technologies as a class, not on a rating review of individual vehicle model implementations. As conceptualized, the system aimed to complement traditional NCAP style ratings as well as to provide consumers with transparent information on early stage and often improving safety technologies. Development of the rating system and identification of data was undertaken in consultation with a range of academic, industrial, consumer, NGO, and governmental experts as well as with representatives of many of the major automotive manufacturers and suppliers. -/- A key observation that emerged was that data on objectively demonstrable real-world benefits were generally sparse and often lower than expectations based on theoretical considerations, simulation studies, or pre-production evaluations. A number of experts and industry representatives expressed some surprise at both the divergence between theoretical and observed benefits and the relative scarcity of data upon which to make objective assessments, while others were quite aware of these issues and the need for the development of objective data under real-world operating conditions. A number of factors that might be relevant to understanding why such differences between expected and observed benefits exist were identified. One outcome of this effort was the founding of the Advanced Vehicle Technology (AVT) consortium to collect and examine objective data under naturalistic driving conditions of how drivers interact with, engage or don’t engage, various production safety and driver assistance systems. This ongoing effort is contributing to insights concerning actual benefits and reasons for benefit gaps. -/- Drawing from our initial work, as well as newer sources of data, we argue that the evaluation and rating of safety and driver assistance technologies for informing the consumer and the public at large should consider both theoretical potential and existing demonstrated benefit of specific technologies. This position is increasingly relevant as the effectiveness of many newer technologies have the potential to actually improve over the lifecycle of a vehicle through software updates. The emphasis on ratings based on observed benefit for actual drivers under real-world conditions is proposed to be complementary, rather than competing with, ratings focused largely on controlled test track evaluations of engineered capability. In addition, a case is made for providing ratings that assesses benefit relative to overall crash, injury, and fatality rates – and in relation to the specific scenario / crash event type that a given technology is intended to address. This approach should aid consumers in considering the extent to which a specific technology is or is not relevant to their particular driving needs. (shrink)

Abstract: The Pull Bayesian Significance Test (FBST) for precise hy- potheses is applied to a Multivariate Normal Structure (MNS) model. In the FBST we compute the evidence against the precise hypothesis. This evi- dence is the probability of the Highest Relative Surprise Set (HRSS) tangent to the sub-manifold (of the parameter space) that defines the null hypothesis. The MNS model we present appears when testing equivalence conditions for genetic expression measurements, using micro-array technology.

Simple random sampling resolutions of the raven paradox relevantly diverge from scientific practice. We develop a stratified random sampling model, yielding a better fit and apparently rehabilitating simple random sampling as a legitimate idealization. However, neither accommodates a second concern, the objection from potential bias. We develop a third model that crucially invokes causal considerations, yielding a novel resolution that handles both concerns. This approach resembles Inference to the Best Explanation (IBE) and relates the generalization’s confirmation to confirmation of an (...) associated law. We give it an objective Bayesian formalization and discuss the compatibility of Bayesianism and IBE. (shrink)

Scientific models typically contain idealizations, or assumptions that are known not to be true. Philosophers have long questioned the nature of idealizations: Are they heuristic tools that will be abandoned? Or rather fictional representations of reality? And how can we reconcile them with realism about knowledge of nature? Immanuel Kant developed an account of scientific investigation that can inspire a new approach to the contemporary debate. Kant argued that scientific investigation is possible only if guided by ideal assumptions—what (...) he calls “regulative ideas”. These ideas are not true of objects of nature, and yet they are not heuristic tools or fictional represen- tations. They are necessary rules governing the construction and assessment of scientific explanations. In this paper, I suggest that some idealizations can be interpreted as having necessary regulative value and as being compatible with scientific realism. I first analyze the puzzle of the nature of idealization and present the main approaches to this topic in the literature. Second, I reconsider the puzzle vis-a-vis a restricted, Kantian definition of idealization and a novel characterization of the relation between idealization and truth. Finally, I discuss in detail an example of idealization (the Hardy-Weinberg equilibrium) along the suggested Kantian lines. (shrink)

Following Nancy Cartwright and others, I suggest that most (if not all) theories incorporate, or depend on, one or more idealizing assumptions. I then argue that such theories ought to be regimented as counterfactuals, the antecedents of which are simplifying assumptions. If this account of the logic form of theories is granted, then a serious problem arises for Bayesians concerning the prior probabilities of theories that have counterfactual form. If no such probabilities can be assigned, the the posterior probabilities will (...) be undefined, as the latter are defined in terms of the former. I argue here that the most plausible attempts to address the problem of probabilities of conditionals fail to help Bayesians, and, hence, that Bayesians are faced with a new problem. In so far as these proposed solutions fail, I argue that Bayesians must give up Bayesianism or accept the counterintuitive view that no theories that incorporate any idealizations have ever really been confirmed to any extent whatsoever. Moreover, as it appears that the latter horn of this dilemma is highly implausible, we are left with the conclusion that Bayesianism should be rejected, at least as it stands. (shrink)

We introduce the notion of complexity, first at an intuitive level and then in relatively more concrete terms, explaining the various characteristic features of complex systems with examples. There exists a vast literature on complexity, and our exposition is intended to be an elementary introduction, meant for a broad audience. -/- Briefly, a complex system is one whose description involves a hierarchy of levels, where each level is made of a large number of components interacting among themselves. The time evolution (...) of such a system is of a complex nature, depending on the interactions among subsystems in the next level below the one under consideration and, at the same time, conditioned by the level above, where the latter sets the context for the evolution. Generally speaking, the interactions among the constituents of the various levels lead to a dynamics characterized by numerous characteristic scales, each level having its own set of scales. What is more, a level commonly exhibits ‘emergent properties’ that cannot be derived from considerations relating to its component systems taken in isolation or to those in a different contextual setting. In the dynamic evolution of some particular level, there occurs a self-organized emergence of a higher level and the process is repeated at still higher levels. -/- The interaction and self-organization of the components of a complex system follow the principle commonly expressed by saying that the ‘whole is different from the sum of the parts’. In the case of systems whose behavior can be expressed mathematically in terms of differential equations this means that the interactions are nonlinear in nature. -/- While all of the above features are not universally exhibited by complex systems, these are nevertheless indicative of a broad commonness relative to which individual systems can be described and analyzed. There exist measures of complexity which, once again, are not of universal applicability, being more heuristic than exact. The present state of knowledge and understanding of complex systems is itself an emerging one. Still, a large number of results on various systems can be related to their complex character, making complexity an immensely fertile concept in the study of natural, biological, and social phenomena. -/- All this puts a very definite limitation on the complete description of a complex system as a whole since such a system can be precisely described only contextually, relative to some particular level, where emergent properties rule out an exact description of more than one levels within a common framework. -/- We discuss the implications of these observations in the context of our conception of the so-called noumenal reality that has a mind-independent existence and is perceived by us in the form of the phenomenal reality. The latter is derived from the former by means of our perceptions and interpretations, and our efforts at sorting out and making sense of the bewildering complexity of reality takes the form of incessant processes of inference that lead to theories. Strictly speaking, theories apply to models that are constructed as idealized versions of parts of reality, within which inferences and abstractions can be carried out meaningfully, enabling us to construct the theories. -/- There exists a correspondence between the phenomenal and the noumenal realities in terms of events and their correlations, where these are experienced as the complex behavior of systems or entities of various descriptions. The infinite diversity of behavior of systems in the phenomenal world are explained within specified contexts by theories. The latter are constructs generated in our ceaseless attempts at interpreting the world, and the question arises as to whether these are reflections of `laws of nature' residing in the noumenal world. This is a fundamental concern of scientific realism, within the fold of which there exists a trend towards the assumption that theories express truths about the noumenal reality. We examine this assumption (referred to as a ‘point of view’ in the present essay) closely and indicate that an alternative point of view is also consistent within the broad framework of scientific realism. This is the view that theories are domain-specific and contextual, and that these are arrived at by independent processes of inference and abstractions in the various domains of experience. Theories in contiguous domains of experience dovetail and interpenetrate with one another, and bear the responsibility of correctly explaining our observations within these domains. -/- With accumulating experience, theories get revised and the network of our theories of the world acquires a complex structure, exhibiting a complex evolution. There exists a tendency within the fold of scientific realism of interpreting this complex evolution in rather simple terms, where one assumes (this, again, is a point of view) that theories tend more and more closely to truths about Nature and, what is more, progress towards an all-embracing ‘ultimate theory’ -- a foundational one in respect of all our inquiries into nature. We examine this point of view closely and outline the alternative view -- one broadly consistent with scientific realism -- that there is no ‘ultimate’ law of nature, that theories do not correspond to truths inherent in reality, and that successive revisions in theory do not lead monotonically to some ultimate truth. Instead, the theories generated in succession are incommensurate with each other, testifying to the fact that a theory gives us a perspective view of some part of reality, arrived at contextually. Instead of resembling a monotonically converging series successive theories are analogous to asymptotic series. -/- Before we summarize all the above considerations, we briefly address the issue of the complexity of the {\it human mind} -- one as pervasive as the complexity of Nature at large. The complexity of the mind is related to the complexity of the underlying neuronal organization in the brain, which operates within a larger biological context, its activities being modulated by other physiological systems, notably the one involving a host of chemical messengers. The mind, with no materiality of its own, is nevertheless emergent from the activity of interacting neuronal assemblies in the brain. As in the case of reality at large, there can be no ultimate theory of the mind, from which one can explain and predict the entire spectrum of human behavior, which is an infinitely rich and diverse one. (shrink)

Philosophers and scientists have often adopted the orthodox version of calibration which involves standardizing an instrument using a known phenomenon. The essential link between theoretical concepts and empirical data, in the philosophy of measurement, has generated the formulation of principles of coordination, synthetic a priori, and revisables. Operationalist thinking wanted to limit the scope of concepts to operations of measurement that are actually achievable. The coherentist perspective in the philosophy of measurement has operated a recovery of coordinationist epistemology and operationalism, (...) relying on a minimal number of ontological principles. Models of an instrument involve a commitment to separation between idealized theories and material things. However, philosophers and metrologists have advocated the requirement of a rich theoretical content in the modeling of measuring instruments. According to other contributions, the epistemic privilege of measurement precedes a background theory and its robustness lies in the free contact with empirical data. Moreover, the applicability regime of a theory dictates its boundary conditions, which guide the experimenter in the design of measuring instruments and provide the basis for operationalizing the meaning of theoretical terms. I support operational pluralism, measurement operations involving different physical indicators, accompanied by dynamic coherentism. The James Webb Space Telescope calibration program is a significant case. Observations of calibration stars by various methods are used to calculate the factors that convert a measurement in instrumental units to physical units. (shrink)